Class Based Thresholding in Early Exit Semantic Segmentation Networks. (arXiv:2210.15621v1 [cs.CV])

We propose Class Based Thresholding (CBT) to reduce the computational cost of
early exit semantic segmentation models while preserving the mean intersection
over union (mIoU) performance. A key idea of CBT is to exploit the
naturally-occurring neural collapse phenomenon. Specifically, by calculating
the mean prediction probabilities of each class in the training set, CBT
assigns different masking threshold values to each class, so that the
computation can be terminated sooner for pixels belonging to easy-to-predict
classes. We show the effectiveness …

arxiv exit networks segmentation semantic thresholding